大規模ストレージシステムにおける高効率かつ高信頼なデータ格納手法に関する研究

早大学位記番号:新7462早稲田大

Universal Deep Image Compression via Content-Adaptive Optimization with Adapters

Deep image compression performs better than conventional codecs, such as JPEG, on natural images. However, deep image compression is learning-based and encounters a problem: the compression performance deteriorates significantly for out-of-domain images. In this study, we highlight this problem and address a novel task: universal deep image compression. This task aims to compress images belonging to arbitrary domains, such as natural images, line drawings, and comics. To address this problem, we propose a content-adaptive optimization framework; this framework uses a pre-trained compression model and adapts the model to a target image during compression. Adapters are inserted into the decoder of the model. For each input image, our framework optimizes the latent representation extracted by the encoder and the adapter parameters in terms of rate-distortion. The adapter parameters are additionally transmitted per image. For the experiments, a benchmark dataset containing uncompressed images of four domains (natural images, line drawings, comics, and vector arts) is constructed and the proposed universal deep compression is evaluated. Finally, the proposed model is compared with non-adaptive and existing adaptive compression models. The comparison reveals that the proposed model outperforms these. The code and dataset are publicly available at https://github.com/kktsubota/universal-dic.Comment: Accepted at the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) 202

eSPRESSO: topological clustering of single-cell transcriptomics data to reveal informative genes for spatio–temporal architectures of cells

[Background] Bioinformatics capability to analyze spatio–temporal dynamics of gene expression is essential in understanding animal development. Animal cells are spatially organized as functional tissues where cellular gene expression data contain information that governs morphogenesis during the developmental process. Although several computational tissue reconstruction methods using transcriptomics data have been proposed, those methods have been ineffective in arranging cells in their correct positions in tissues or organs unless spatial information is explicitly provided. [Results] This study demonstrates stochastic self-organizing map clustering with Markov chain Monte Carlo calculations for optimizing informative genes effectively reconstruct any spatio–temporal topology of cells from their transcriptome profiles with only a coarse topological guideline. The method, eSPRESSO (enhanced SPatial REconstruction by Stochastic Self-Organizing Map), provides a powerful in silico spatio–temporal tissue reconstruction capability, as confirmed by using human embryonic heart and mouse embryo, brain, embryonic heart, and liver lobule with generally high reproducibility (average max. accuracy = 92.0%), while revealing topologically informative genes, or spatial discriminator genes. Furthermore, eSPRESSO was used for temporal analysis of human pancreatic organoids to infer rational developmental trajectories with several candidate ‘temporal’ discriminator genes responsible for various cell type differentiations. [Conclusions] eSPRESSO provides a novel strategy for analyzing mechanisms underlying the spatio–temporal formation of cellular organizations

Prediction of Boron Concentrations in Blood from Patients on Boron Neutron Capture Therapy

Background: In boron neutron capture therapy, blood boron concentration is the key factor to calculate radiation dose, however, blood sampling is difficult during neutron irradiation. Materials and Methods: The prediction of blood boron concentrations for BNCT treatment planning has been prospectively investigated using patient data obtained at first craniotomy after the infusion of a low dose of sodium undecahydroclosododecaborate. Results: The boron biodistribution data showed a biexponential pharmacokinetic profile. If the final boron concentration at 6 or 9 hours after the end of the infusion is within the 95% confidence interval of the prediction, direct prediction from biexponential fit will reduce the error of blood boron concentrations during irradiation to around 6%. Conclusion: Actual boron concentrations during BNCT were reasonably and accurately predictable from the test data

Impact of Native Coronary Artery Calcification on Lesion Outcome Following Drug-Coated Balloon Angioplasty for Treatment of In-Stent Restenosis

This study aimed to clarify whether native coronary artery（CA） calcification before index percutaneous coronary intervention（PCI） has an impact on the effectiveness of drug-coated balloon（DCB） angioplasty for the treatment of in-stent restenosis（ISR）. 100consecutive patients with 166ISR lesions underwent quantitative coronary angiography（QCA） before and after index PCI and before and after DCB angioplasty for ISR. CA calcification before index PCI was assessed by angiography and results were analyzed to reveal the predictive values for target lesion revascularization（TLR） and major adverse cardiac events（MACE）. During 1.03±1.03years of follow-up, TLR occurred in 44lesions（26.5%） and MACE in 33 patients（33%）. On multivariate analysis, CA calcification before index PCI（p=0.016）, and % diameter of stenosis（%DS）≥73%（p=0.023） and minimal lumen diameter（MLD）<0.65mm（p=0.001） before DCB angioplasty were independent predictors for TLR after DCB angioplasty. MACE was also associated with CA calcification before index PCI（p=0.01）, and %DS≥73%（p=0.001） and MLD<0.65mm（p=0.01） before DCB angioplasty, but only %DS≥73% before DCB angioplasty was an independent predictor for MACE after DCB angioplasty（p=0.039）. The combination of CA calcification before index PCI and these QCA factors before DCB angioplasty was an independent and more powerful predictor for MACE than the QCA factors alone（p<0.001）. Thereafter, the combination of CA calcification and %DS≥73% before DCB angioplasty stratified the risk of MACE after DCB angioplasty（p<0.05）. CA calcification before index PCI, as well as anatomical information at ISR, have an impact on outcome after DCB angioplasty for ISR

Significance of Coronary Artery Calcium Score in the Target Lesion Evaluated by Multi-detector Computed Tomography for Selecting Treatment of Rotational Atherectomy in Patients with Coronary Artery Disease

We investigated whether coronary artery calcium score (CAC) in the target lesion on the multidetector computed tomography angiography (CTA) predicts the addition of the Rotational atherectomy (Rota) during percutaneous coronary intervention (PCI). Lesion CAC on CTA were evaluated with quantitative coronary analysis (QCA) on coronary angiography for predicting the Rota treatment in 114 consecutive patients (165 target lesions) with first PCI (68 ± 9 years old, females: 17.6%). Rota was added in 8 patients (11 lesions). The lesion length and diameter stenosis on QCA, and lesion length and lesion CAC on CTA were the primary factors associated with the addition of Rota. Using the cut-off value based on receiver operating characteristic analysis, the sensitivity and specificity for predicting the Rota based on QCA was 72.7% in 8 of 11 lesions (vessels) with Rota and the specificity was 74% in 114 of 154 without Rota in the lesion length of ≥ 23mm (χ2=10.9, p=0.001), and 54.5% in 6 of 11 lesions with Rota and the specificity was 79.2% in 122 of 154 without Rota in the diameter stenosis of ≥ 83% (χ2=6.6, p=0.01). Those based on CTA were 90.9% in 10 of 11 lesions with Rota and 77.3% in 119 of 154 without Rota in the lesion length of ≥ 34mm (χ2=24.1, p<0.001), and 90.9% in 10 of 11 with Rota and 88.3% in 136 of 154 without Rota in the lesions with CAC ≥453 (χ2=45.7, p<0.001). Lesion CAC on CTA is most predictive of addition of Rota during PCI